{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting ./train-images-idx3-ubyte.gz\n",
      "Extracting ./train-labels-idx1-ubyte.gz\n",
      "Extracting ./t10k-images-idx3-ubyte.gz\n",
      "Extracting ./t10k-labels-idx1-ubyte.gz\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x576 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import cv2 as cv\n",
    "import numpy as np\n",
    "import tensorflow as tf \n",
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "tf.logging.set_verbosity(tf.logging.INFO)\n",
    "\n",
    "mnist = input_data.read_data_sets(\"./\",one_hot=True)\n",
    "\n",
    "plt.figure(figsize=(8,8))\n",
    "\n",
    "for idx in range(16):\n",
    "    plt.subplot(4,4,idx+1)\n",
    "    plt.axis(\"off\")\n",
    "    plt.title(\"{}\".format(np.argmax(mnist.train.labels[idx])))\n",
    "    plt.imshow(mnist.train.images[idx].reshape(28,28))\n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "W0818 15:29:14.859754 140657989310272 deprecation.py:323] From <ipython-input-3-0c4c10737790>:53: softmax_cross_entropy_with_logits (from tensorflow.python.ops.nn_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "\n",
      "Future major versions of TensorFlow will allow gradients to flow\n",
      "into the labels input on backprop by default.\n",
      "\n",
      "See `tf.nn.softmax_cross_entropy_with_logits_v2`.\n",
      "\n",
      "W0818 15:29:15.159866 140657989310272 deprecation.py:323] From <ipython-input-3-0c4c10737790>:69: arg_max (from tensorflow.python.ops.gen_math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use `tf.math.argmax` instead\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "after 100 training steps, the loss is 0.322421, the validation accuracy is 0.8854\n",
      "after 200 training steps, the loss is 0.261618, the validation accuracy is 0.9266\n",
      "after 300 training steps, the loss is 0.290936, the validation accuracy is 0.9392\n",
      "after 400 training steps, the loss is 0.227304, the validation accuracy is 0.946\n",
      "after 500 training steps, the loss is 0.601851, the validation accuracy is 0.9164\n",
      "after 600 training steps, the loss is 0.06048, the validation accuracy is 0.9644\n",
      "after 700 training steps, the loss is 0.13219, the validation accuracy is 0.9658\n",
      "after 800 training steps, the loss is 0.061535, the validation accuracy is 0.97\n",
      "after 900 training steps, the loss is 0.0685227, the validation accuracy is 0.9672\n",
      "after 1000 training steps, the loss is 0.142354, the validation accuracy is 0.9702\n",
      "after 1100 training steps, the loss is 0.0412335, the validation accuracy is 0.9748\n",
      "after 1200 training steps, the loss is 0.0800135, the validation accuracy is 0.9716\n",
      "after 1300 training steps, the loss is 0.109809, the validation accuracy is 0.9654\n",
      "after 1400 training steps, the loss is 0.0137528, the validation accuracy is 0.9724\n",
      "after 1500 training steps, the loss is 0.114637, the validation accuracy is 0.9736\n",
      "after 1600 training steps, the loss is 0.158806, the validation accuracy is 0.9656\n",
      "after 1700 training steps, the loss is 0.0786514, the validation accuracy is 0.978\n",
      "after 1800 training steps, the loss is 0.0493253, the validation accuracy is 0.9766\n",
      "after 1900 training steps, the loss is 0.0237395, the validation accuracy is 0.9762\n",
      "after 2000 training steps, the loss is 0.0425623, the validation accuracy is 0.9746\n",
      "after 2100 training steps, the loss is 0.0419038, the validation accuracy is 0.9754\n",
      "after 2200 training steps, the loss is 0.0327657, the validation accuracy is 0.9752\n",
      "after 2300 training steps, the loss is 0.0162767, the validation accuracy is 0.9768\n",
      "after 2400 training steps, the loss is 0.0246468, the validation accuracy is 0.9786\n",
      "after 2500 training steps, the loss is 0.0194252, the validation accuracy is 0.9782\n",
      "after 2600 training steps, the loss is 0.0153425, the validation accuracy is 0.979\n",
      "after 2700 training steps, the loss is 0.0685994, the validation accuracy is 0.9776\n",
      "after 2800 training steps, the loss is 0.0121109, the validation accuracy is 0.9794\n",
      "after 2900 training steps, the loss is 0.0279605, the validation accuracy is 0.9758\n",
      "after 3000 training steps, the loss is 0.00440261, the validation accuracy is 0.9806\n",
      "after 3100 training steps, the loss is 0.0197642, the validation accuracy is 0.977\n",
      "after 3200 training steps, the loss is 0.0522748, the validation accuracy is 0.9764\n",
      "after 3300 training steps, the loss is 0.0796514, the validation accuracy is 0.9746\n",
      "after 3400 training steps, the loss is 0.000833491, the validation accuracy is 0.9796\n",
      "after 3500 training steps, the loss is 0.001342, the validation accuracy is 0.9808\n",
      "after 3600 training steps, the loss is 0.0734819, the validation accuracy is 0.9798\n",
      "after 3700 training steps, the loss is 0.0026782, the validation accuracy is 0.9794\n",
      "after 3800 training steps, the loss is 0.00557262, the validation accuracy is 0.977\n",
      "after 3900 training steps, the loss is 0.00963754, the validation accuracy is 0.9804\n",
      "after 4000 training steps, the loss is 0.0222895, the validation accuracy is 0.9742\n",
      "after 4100 training steps, the loss is 0.000805098, the validation accuracy is 0.9814\n",
      "after 4200 training steps, the loss is 0.00250935, the validation accuracy is 0.979\n",
      "after 4300 training steps, the loss is 0.00115685, the validation accuracy is 0.9776\n",
      "after 4400 training steps, the loss is 0.0060363, the validation accuracy is 0.979\n",
      "after 4500 training steps, the loss is 0.0068274, the validation accuracy is 0.9824\n",
      "after 4600 training steps, the loss is 0.000839526, the validation accuracy is 0.9822\n",
      "after 4700 training steps, the loss is 0.0590759, the validation accuracy is 0.9734\n",
      "after 4800 training steps, the loss is 0.00313585, the validation accuracy is 0.9814\n",
      "after 4900 training steps, the loss is 0.00154177, the validation accuracy is 0.98\n",
      "training is finished!\n",
      "the test accuracy is  0.9804\n"
     ]
    }
   ],
   "source": [
    "x = tf.placeholder(tf.float32, [None, 784], name=\"x\")\n",
    "y = tf.placeholder(tf.float32,[None, 10], name=\"y\")\n",
    "learning_rate = tf.placeholder(tf.float32)\n",
    "\n",
    "def initialize(shape, stddev = 0.12):  # 由0.1 --> 0.2 修改初始化方式\n",
    "    return tf.truncated_normal(shape, stddev=0.1)\n",
    "\n",
    "# 第一层隐层的神经元个数,修改神经元个数：100-->150\n",
    "L1_units_count = 400\n",
    "\n",
    "W_1 = tf.Variable(initialize([784,L1_units_count]))\n",
    "b_1 = tf.Variable(initialize([L1_units_count]))\n",
    "\n",
    "logits_1 = tf.matmul(x, W_1) + b_1\n",
    "output_1 = tf.nn.relu(logits_1)\n",
    "\n",
    "# 多加一层隐层\n",
    "L2_units_count = 350\n",
    "\n",
    "W_2 = tf.Variable(initialize([L1_units_count,L2_units_count]))\n",
    "b_2 = tf.Variable(initialize([L2_units_count]))\n",
    "\n",
    "logits_2 = tf.matmul(output_1, W_2) + b_2\n",
    "output_2 = tf.nn.relu(logits_2)\n",
    "\n",
    "# 多加第二层隐层\n",
    "L3_units_count = 200\n",
    "\n",
    "W_3 = tf.Variable(initialize([L2_units_count,L3_units_count]))\n",
    "b_3 = tf.Variable(initialize([L3_units_count]))\n",
    "\n",
    "logits_3 = tf.matmul(output_2, W_3) + b_3\n",
    "output_3 = tf.nn.relu(logits_3)\n",
    "\n",
    "# 第四层隐层的神经元个数\n",
    "L4_units_count = 200\n",
    "\n",
    "W_4 = tf.Variable(initialize([L3_units_count,L4_units_count]))\n",
    "b_4 = tf.Variable(initialize([L4_units_count]))\n",
    "\n",
    "logits_4 = tf.matmul(output_3, W_4) + b_4\n",
    "output_4 = tf.nn.relu(logits_4)\n",
    "\n",
    "# 第五层隐层\n",
    "L5_units_count = 10\n",
    "\n",
    "W_5 = tf.Variable(initialize([L4_units_count,L5_units_count]))\n",
    "b_5 = tf.Variable(initialize([L5_units_count]))\n",
    "\n",
    "logits_5 = tf.matmul(output_4, W_5) + b_5\n",
    "logits = logits_5  # 最后一层未激活\n",
    "\n",
    "cross_entropy_loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=y))\n",
    "\n",
    "# 加正则项\n",
    "# tf.add_n( [ list ] ): 求list中元素的累加和，列表里的元素可以是向量，矩阵等\n",
    "l2_loss = tf.add_n(\n",
    "    [\n",
    "        tf.nn.l2_loss(w)\n",
    "        # tf.get_collection('losses'):获取集合“losses”中的所有元素，生成一个列表并返回该列表\n",
    "        for w in tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES)\n",
    "    ]\n",
    ")\n",
    "\n",
    "total_loss = cross_entropy_loss + 7e-5 * l2_loss\n",
    "optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate).minimize(total_loss)\n",
    "\n",
    "pred = tf.nn.softmax(logits)\n",
    "correct_pred = tf.equal(tf.arg_max(pred,1),tf.arg_max(y,1))\n",
    "accuray = tf.reduce_mean(tf.cast(correct_pred, tf.float32))\n",
    "\n",
    "batchsize = 100\n",
    "training_step = 5000\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    sess.run(tf.global_variables_initializer())\n",
    "    \n",
    "    # 定义验证集与测试集\n",
    "    validate_data = {\n",
    "        x: mnist.validation.images,\n",
    "        y: mnist.validation.labels\n",
    "    }\n",
    "    test_data = {\n",
    "        x: mnist.test.images,\n",
    "        y: mnist.test.labels\n",
    "    }\n",
    "    \n",
    "    for i in range(training_step):\n",
    "        xs, ys = mnist.train.next_batch(batchsize)\n",
    "        _,loss = sess.run([optimizer,cross_entropy_loss],\n",
    "                         feed_dict={\n",
    "                             x:xs,\n",
    "                             y:ys,\n",
    "                             learning_rate:0.3\n",
    "                         })\n",
    "        \n",
    "        # 每100次训练打印一次损失值与验证准确率\n",
    "        if i >0 and i%100 == 0:\n",
    "            validate_accuracy = sess.run(accuray,feed_dict=validate_data)\n",
    "            print(\"after %d training steps, the loss is %g, the validation accuracy is %g\" % (i,loss, validate_accuracy))\n",
    "            \n",
    "    print(\"training is finished!\")\n",
    "    \n",
    "    acc = sess.run(accuray,feed_dict=test_data)\n",
    "    print(\"the test accuracy is \", acc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. 正常的log输出 ，log中的模型准确率达到98"
   ]
  },
  {
   "attachments": {
    "%E5%9B%BE%E7%89%87.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![%E5%9B%BE%E7%89%87.png](attachment:%E5%9B%BE%E7%89%87.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. 如何修改隐层数量，修改后会起到什么样的效果"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "答：修改隐层数量，就是增加神经网络的深度，根据前面神经元的个数以及本层设计的神经元个数确定w的shape，b的shape。\n",
    "\n",
    "   修改隐层数量也就是增加了神经网络的深度，达到的效果是使网络的抽象能力更强，学习到的特征更加复杂，结果的准确率越高。\n",
    "   \n",
    "   一般认为，增加隐层数可以降低网络误差（也有文献认为不一定能有效降低），提高精度，但也使网络复杂化，从而增加了网络的训练时间和出现“过拟合”的倾向。一般来讲应设计神经网络应优先考虑3层网络（即有1个隐层）。一般地，靠增加隐层节点数来获得较低的误差，其训练效果要比增加隐层数更容易实现。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. 如何修改神经元个数，起到了什么样的效果"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "答：修改神经元个数，可以通过对网络结构中的w的shape，b的shape进行修改。\n",
    "\n",
    "    修改神经元个数相当于增加了权重参数的个数，适当的增加神经元个数可以提高网络的准确率和精度，但是过多的神经元也是导致过拟合的“元凶”。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. 如何在模型中添加L1/L2正则化，正则化起什么作⽤"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "答：通过tensorflow的api获得L1/L2正则如下\n",
    "\n",
    "l2_loss = tf.add_n(\n",
    "    [\n",
    "        tf.nn.l2_loss(w)\n",
    "        for w in tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES)\n",
    "    ]\n",
    ")\n",
    "\n",
    "然后将其乘以一个超参数再与交叉熵损失相加构成最终的损失函数\n",
    "\n",
    "    正则化的作用：防止神经网络过度学习数据而造成过拟合现象，可以限制权重w值的大小"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. 使⽤不同的初始化⽅式对模型有什么影响"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用不同的初始化方式可以让训练之初的效果产生差异，比如让收敛速度更快。\n",
    "\n",
    "特别的，对于含有局部极小值的情况，通过不同的初始化方式，有可能更加逼近最小值。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
